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Compressed Sensing and its Applications

that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as;

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Compressed Sensing und Sparse Rekonstruktion bei Magnetic Particle Imaging

erstellt, indem die Systemantwort auf eine Punktprobe in jedem Voxel gemessen wird. Die Theorie des Compressed Sensing besagt, dass ein Signal;

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Compressed Sensing for Distributed Systems

This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has;

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Compressed Sensing in Li-Fi and Wi-Fi Networks

Compressed Sensing in Li-Fi and Wi-Fi Networks features coverage of the first applications in optical telecommunications and wireless;

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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Compressed Sensing for Engineers

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations;

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Compressed Sensing in Radar Signal Processing

highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it;

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Compressive Sensing for Wireless Communication

Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements;

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Compressed Sensing

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics;

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Compressed Sensing for MRI

This work explores and extends the concept of applying compressed sensing to MRI. Asuccessful CS reconstruction requires incoherent;

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Compressive Sensing of Earth Observations

Future remote sensing systems will make extensive use of Compressive Sensing (CS) as it becomes more integrated into the system design with;

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic;

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Computing in Communication Networks

software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or;

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Image Understanding Using Sparse Representations

, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the;

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Reconstruction-Free Compressive Vision for Surveillance Applications

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the;

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Reconstruction-Free Compressive Vision for Surveillance Applications

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the;

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Statistical Methods for Materials Science

applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.;

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Compressive Sensing for Urban Radar

With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed;

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Compressive Sensing for Urban Radar

With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed;

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Statistical Methods for Materials Science: The Data Science of Microstructure Characterization

applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.;

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Advances in Sampling Theory and Techniques

of signal integral transforms. Topics include classical sampling theory, compressed sensing, non-redundant sampling, fast signal resampling;

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Modeling Nanoscale Imaging in Electron Microscopy

denoising methods, application of mathematical/statistical learning theory, and compressed sensing.;

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Financial Signal Processing and Machine Learning

portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures;

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Image and Video Compression

the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most;

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Image and Video Compression

the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most;

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Compressed Air - Practical Information Upon Air-Compression And The Transmission And Application Of Compressed Air

of compressed air, and the wide diffusion of misinfor- mation and prejudice upon this subject. In spite of it all the use of compressed air is rapidly;

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